Auto-Grouped Sparse Representation for Visual Analysis

  • Jiashi Feng
  • Xiaotong Yuan
  • Zilei Wang
  • Huan Xu
  • Shuicheng Yan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7572)


In this work, we investigate how to automatically uncover the underlying group structure of a feature vector such that each group characterizes certain object-specific patterns, e.g., visual pattern or motion trajectories from one object. By mining the group structure, we can effectively alleviate the mutual inference of multiple objects and improve the performance in various visual analysis tasks. To this end, we propose a novel auto-grouped sparse representation (ASR) method. ASR groups semantically correlated feature elements together through optimally fusing their multiple sparse representations. Due to the intractability of primal objective function, we also propose well-behaved convex relaxation and smooth approximation to guarantee obtaining a global optimal solution effectively. Finally, we apply ASR in two important visual analysis tasks: multi-label image classification and motion segmentation. Comprehensive experimental evaluations show that ASR is able to achieve superior performance compared with the state-of-the-arts on these two tasks.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Jiashi Feng
    • 1
  • Xiaotong Yuan
    • 2
  • Zilei Wang
    • 3
  • Huan Xu
    • 4
  • Shuicheng Yan
    • 1
  1. 1.Department of ECENational University of SingaporeSingapore
  2. 2.Department of StatisticsRutgers UniversityUSA
  3. 3.Department of AutomationUniversity of Science and Technology of ChinaChina
  4. 4.Department of MENational University of SingaporeSingapore

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